Dendritic cell visualise quality control
Daniel Brown
2024-06-18
TurboCapture-Seq mouse dendritic cells
Recap
Prior 96w evaluation of TurboCapture-Seq v2 showed low UMIs recovered and high seq saturation. I did low throughput troubleshooting and didn’t see any issues. Process this experiment myself taking care to remove residual liquids from wash steps.
Process Hui Shi of Naik lab Flt3 stimulated DC timecourse. Includes a few of my samples.
Samples
- Sorted dendritic cells
- HEK293T cell lysates in 1x TCL @ cells/uL
- PBMC cell lysates in 1x TCL @ 500 cells/uL
- No template control 1x TCL
Notebook recap
SCE object in generate in 1A_generateSCE notebook.
Notebook aim
Plot the QC metrics.
Check on plate map representation
Plot on plate map Orientation is correct as low counts are where the no template control should be
p1 <- platetools::raw_map(data=tb$Cell_number,
well=tb$Well,
plate=96) + ggtitle("Cell input number")
p2 <- platetools::raw_map(data=log(tb$sum+1),
well=tb$Well,
plate=96) + ggtitle("Log library size")
p1 / p2
Remove NA samples
High expressing genes
High expressors
Well under 1 million counts which is the UMI n = 10 length. We are within the dynamic range of the assay.
Look at the individual genes
plotExpression(sce, features = c("Gm10076", "Tmsb10", "Rps3", "Fau"), colour_by = "Ligand", exprs_values = "counts") +
theme_Publication() + scale_colour_Publication()
Library size and gene plots
Library size log
The text represents the timepoint. No clear pattern to me except maybe the genotype.
plt1 <- ggplot(tb,
aes(x = Cell_type, y= sum, colour = Genotype, label = Timepoint_Day, shape=Ligand)) +
geom_text() +
ylab("Library Size (UMIs)") +
xlab("Lysis buffer") +
scale_y_continuous(trans='log10') +
annotation_logticks(base = 10, sides = "l") +
scale_fill_brewer(palette = "Dark2")
plt1
Library size in deduplicated UMIs
Library size natural scale
plt2 <- ggplot(tb,
aes(x = Cell_type, y= sum, colour = Genotype, label = Timepoint_Day, shape=Ligand)) +
geom_text() +
ylab("Library Size (UMIs)") +
xlab("Lysis buffer") +
scale_fill_brewer(palette = "Dark2")
plt2
Library size in deduplicated UMIs
Detected genes
plt3 <- ggplot(tb,
aes(x = Cell_type, y= detected, colour = Genotype, label = Timepoint_Day, shape=Ligand)) +
geom_text() +
ylab("Genes detected") +
xlab("Lysis buffer") +
scale_fill_brewer(palette = "Dark2")
plt3
Number of detected genes
Genes v UMIs
Not much pattern here except HEKs and PBMCs from me. Fewer genes detected across comparable UMIs.
plt4 <- ggplot(tb,
aes(x = detected+1, y= sum+1, colour = Cell_type, label=Well)) +
geom_text() +
ylab("Library size (UMIs)") +
xlab("Genes detected") +
scale_y_continuous(trans='log10') + scale_x_continuous(trans='log10') +
annotation_logticks(base = 10, sides = "bl")
plt4
UMIs versus genes
Mitochondrial genes
Very low percentage for everything. Cell health looks good.
plt5 <- ggplot(tb,
aes(x = Cell_type, y= subsets_Mito_percent, colour = Genotype, label = Timepoint_Day, shape=Ligand)) +
geom_text() +
ylab("Percent mouse mitochondrial genes") +
xlab("Lysis buffer") +
scale_fill_brewer(palette = "Dark2")
plt5
Percent mouse mitochondrial counts in library
Conclusion
Quality of the sample looks good to me. Will further check by clustering samples
Session info
## R version 4.4.0 (2024-04-24)
## Platform: x86_64-pc-linux-gnu
## Running under: CentOS Linux 7 (Core)
##
## Matrix products: default
## BLAS: /stornext/System/data/apps/R/R-4.4.0/lib64/R/lib/libRblas.so
## LAPACK: /stornext/System/data/apps/R/R-4.4.0/lib64/R/lib/libRlapack.so; LAPACK version 3.12.0
##
## locale:
## [1] LC_CTYPE=en_US.UTF-8 LC_NUMERIC=C
## [3] LC_TIME=en_US.UTF-8 LC_COLLATE=en_US.UTF-8
## [5] LC_MONETARY=en_US.UTF-8 LC_MESSAGES=en_US.UTF-8
## [7] LC_PAPER=en_US.UTF-8 LC_NAME=C
## [9] LC_ADDRESS=C LC_TELEPHONE=C
## [11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C
##
## time zone: Australia/Melbourne
## tzcode source: system (glibc)
##
## attached base packages:
## [1] grid stats4 stats graphics grDevices utils datasets
## [8] methods base
##
## other attached packages:
## [1] scales_1.3.0 ggthemes_5.1.0
## [3] here_1.0.1 patchwork_1.2.0
## [5] platetools_0.1.7 scater_1.32.0
## [7] scuttle_1.14.0 lubridate_1.9.3
## [9] forcats_1.0.0 stringr_1.5.1
## [11] dplyr_1.1.4 purrr_1.0.2
## [13] readr_2.1.5 tidyr_1.3.1
## [15] tibble_3.2.1 ggplot2_3.5.1
## [17] tidyverse_2.0.0 SingleCellExperiment_1.26.0
## [19] SummarizedExperiment_1.34.0 Biobase_2.64.0
## [21] GenomicRanges_1.56.0 GenomeInfoDb_1.40.0
## [23] IRanges_2.38.0 S4Vectors_0.42.0
## [25] BiocGenerics_0.50.0 MatrixGenerics_1.16.0
## [27] matrixStats_1.3.0
##
## loaded via a namespace (and not attached):
## [1] tidyselect_1.2.1 viridisLite_0.4.2
## [3] farver_2.1.2 vipor_0.4.7
## [5] viridis_0.6.5 fastmap_1.2.0
## [7] digest_0.6.35 rsvd_1.0.5
## [9] timechange_0.3.0 lifecycle_1.0.4
## [11] magrittr_2.0.3 compiler_4.4.0
## [13] rlang_1.1.3 sass_0.4.9
## [15] tools_4.4.0 utf8_1.2.4
## [17] yaml_2.3.8 knitr_1.46
## [19] labeling_0.4.3 S4Arrays_1.4.0
## [21] DelayedArray_0.30.1 RColorBrewer_1.1-3
## [23] abind_1.4-5 BiocParallel_1.38.0
## [25] withr_3.0.0 fansi_1.0.6
## [27] beachmat_2.20.0 colorspace_2.1-0
## [29] cli_3.6.2 rmarkdown_2.26
## [31] crayon_1.5.2 generics_0.1.3
## [33] rstudioapi_0.16.0 httr_1.4.7
## [35] tzdb_0.4.0 DelayedMatrixStats_1.26.0
## [37] ggbeeswarm_0.7.2 cachem_1.1.0
## [39] zlibbioc_1.50.0 parallel_4.4.0
## [41] XVector_0.44.0 vctrs_0.6.5
## [43] Matrix_1.7-0 jsonlite_1.8.8
## [45] BiocSingular_1.20.0 hms_1.1.3
## [47] BiocNeighbors_1.22.0 ggrepel_0.9.5
## [49] irlba_2.3.5.1 beeswarm_0.4.0
## [51] jquerylib_0.1.4 glue_1.7.0
## [53] codetools_0.2-20 cowplot_1.1.3
## [55] stringi_1.8.4 gtable_0.3.5
## [57] UCSC.utils_1.0.0 ScaledMatrix_1.12.0
## [59] munsell_0.5.1 pillar_1.9.0
## [61] htmltools_0.5.8.1 GenomeInfoDbData_1.2.12
## [63] R6_2.5.1 sparseMatrixStats_1.16.0
## [65] rprojroot_2.0.4 evaluate_0.23
## [67] lattice_0.22-6 highr_0.10
## [69] bslib_0.7.0 Rcpp_1.0.12
## [71] gridExtra_2.3 SparseArray_1.4.3
## [73] xfun_0.44 pkgconfig_2.0.3